This demo is an implementation of starting the streaming speech synthesis service and accessing the service. It can be achieved with a single command using `paddlespeech_server` and `paddlespeech_client` or a few lines of code in python.
## Usage
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
-`engine_list` indicates the speech engine that will be included in the service to be started, in the format of `<speech task>_<engine type>`.
- This demo mainly introduces the streaming speech synthesis service, so the speech task should be set to `tts`.
- the engine type supports two forms: **online** and **online-onnx**. `online` indicates an engine that uses python for dynamic graph inference; `online-onnx` indicates an engine that uses onnxruntime for inference. The inference speed of online-onnx is faster.
- Streaming TTS engine AM model support: **fastspeech2 and fastspeech2_cnndecoder**; Voc model support: **hifigan and mb_melgan**
- In streaming am inference, one chunk of data is inferred at a time to achieve a streaming effect. Among them, `am_block` indicates the number of valid frames in the chunk, and `am_pad` indicates the number of frames added before and after am_block in a chunk. The existence of am_pad is used to eliminate errors caused by streaming inference and avoid the influence of streaming inference on the quality of synthesized audio.
- fastspeech2 does not support streaming am inference, so am_pad and am_block have no effect on it.
- fastspeech2_cnndecoder supports streaming inference. When am_pad=12, streaming inference synthesized audio is consistent with non-streaming synthesized audio.
- In streaming voc inference, one chunk of data is inferred at a time to achieve a streaming effect. Where `voc_block` indicates the number of valid frames in the chunk, and `voc_pad` indicates the number of frames added before and after the voc_block in a chunk. The existence of voc_pad is used to eliminate errors caused by streaming inference and avoid the influence of streaming inference on the quality of synthesized audio.
- Both hifigan and mb_melgan support streaming voc inference.
- When the voc model is mb_melgan, when voc_pad=14, the synthetic audio for streaming inference is consistent with the non-streaming synthetic audio; the minimum voc_pad can be set to 7, and the synthetic audio has no abnormal hearing. If the voc_pad is less than 7, the synthetic audio sounds abnormal.
- When the voc model is hifigan, when voc_pad=19, the streaming inference synthetic audio is consistent with the non-streaming synthetic audio; when voc_pad=14, the synthetic audio has no abnormal hearing.
- **Note:** If the service can be started normally in the container, but the client access IP is unreachable, you can try to replace the `host` address in the configuration file with the local IP address.
-`play`: Whether to play audio, play while synthesizing, default value: False, which means not playing. **Playing audio needs to rely on the pyaudio library**.
- Currently, only the single-speaker model is supported in the code, so `spk_id` does not take effect. Streaming TTS does not support changing sample rate, variable speed and volume.
-`play`: Whether to play audio, play while synthesizing, default value: False, which means not playing. **Playing audio needs to rely on the pyaudio library**.
- Currently, only the single-speaker model is supported in the code, so `spk_id` does not take effect. Streaming TTS does not support changing sample rate, variable speed and volume.
